This article introduces the notions of functional space and concept as a way of knowledge representation and abstraction for Reinforcement Learning agents. These definitions are used as a tool of knowledge transfer among agents. The agents are assumed to be heterogeneous; they have different state spaces but share a same dynamic, reward and action space. In other words, the agents are assumed t...

We address the problem of constructing multiagent systems by coordinating heterogeneous , autonomous agents, whose internal designs may not be fully known. We develop a customizable coordination service that (a) takes declarative speciications of the desired interactions, and (b) automatically enacts them. Our approach is based on temporal logic, and has a rigorous semantics and a naturally dis...

In this paper, we present a multi agent system (MAS) simulation of a financial market and investigate the requirements to obtain realistic data. The model consists of autonomous, interactive agents that buy stock on a financial market. Transaction decisions are based on a number of individual and collective elements. The former being risk aversion and a set of decision rules reflecting their an...

In [17], two of the authors have introduced techniques to build agents on top of arbitrary data structures, and to “agentize” new/existing programs. They provided a series of successively more sophisticated semantics for such agent systems, and showed that as these semantics become epistemically more desirable, a computational price may need to be paid. In this paper, we identify a class of age...

One of the most interesting issues that arises in agents based on the BDI (belief-desire-intention) formalism is capturing notions of realism, that is constraints that describe possible interrelationships between the three attitudes. Three such sets of constraints have been considered in the literature: strong realism, realism and weak realism. In this paper we propose notions of realism for he...